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embeddings_pooling
ai-agents-embeddings-pooling-using-llamacppembeddings-with-a-gguf-model-that-re-e407a4b4
IFUsing LlamaCppEmbeddings with a GGUF model that returns token-level (list-of-lists) embeddings instead of a single vector per document causes TypeError: float() argument must be a string or a real number, not 'list'.
THENFlatten the nested embeddings by iterating over the inner lists and converting each to float, or pool token embeddings (e.g., average) to produce a single vector per document. The recommended fix is to change line 114 in llama.cpp to: `return [list(map(float, sublist)) for e in embeddings for sublist in e]` which concatenates all token vectors into one flat list per document. However, consider using a model that supports sequence-level embeddings or applying pooling yourself for better semantic representation.
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